Urban-rural difference in the lagged effects of PM2.5 and PM10 on COPD mortality in Chongqing, China.

BACKGROUND: It is true that Chronic obstructive pulmonary disease (COPD) will increase social burden, especially in developing countries. Urban-rural differences in the lagged effects of PM2.5 and PM10 on COPD mortality remain unclear, in Chongqing, China. METHODS: In this study, a distributed lag non-linear model (DLNMs) was established to describe the urban-rural differences in the lagged effects of PM2.5, PM10 and COPD mortality in Chongqing, using 312,917 deaths between 2015 and 2020. RESULTS: According to the DLNMs results, COPD mortality in Chongqing increases with increasing PM2.5 and PM10 concentrations, and the relative risk (RR) of the overall 7-day cumulative effect is higher in rural areas than in urban areas. High values of RR in urban areas occurred at the beginning of exposure (Lag 0 ~ Lag 1). High values of RR in rural areas occur mainly during Lag 1 to Lag 2 and Lag 6 to Lag 7. CONCLUSION: Exposure to PM2.5 and PM10 is associated with an increased risk of COPD mortality in Chongqing, China. COPD mortality in urban areas has a high risk of increase in the initial phase of PM2.5 and PM10 exposure. There is a stronger lagging effect at high concentrations of PM2.5 and PM10 exposure in rural areas, which may further exacerbate inequalities in levels of health and urbanization.

Spatial association between green space and COPD mortality: a township-level ecological study in Chongqing, China.

BACKGROUND: There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. METHODS: After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. RESULTS: The results show that the FVC local regression coefficients ranged from - 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. CONCLUSIONS: Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.

Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.

Binary-tree encryption strategy for optical multiple-image encryption.

In traditional optical multiple-image encryption schemes, different images typically have almost the same encryption or decryption process. Provided that an attacker manages to correctly decrypt some image, the conventional attacks upon other images are much easier to be made. In this paper, a binary-tree encryption strategy for multiple images is proposed to resist the attacks in this case. The encryption schemes produced by this strategy can not only increase the security of multiple-image encryption, but also realize an authority management with high security among the users sharing a cipher image. For a simulation test, we devise a basic binary-tree encryption scheme, whose encryption nodes are based on an asymmetric double random phase encoding in the gyrator domain. The favorable simulation results about the tested scheme can testify to the feasibility of the strategy.

Optical compression and encryption system combining multiple measurement matrices with fractional Fourier transform.

Optical cryptosystems combined with compressed sensing can achieve compression and encryption simultaneously. But they usually use the same measurement matrix to sample all blocks of an image, which makes it easy to estimate the measurement matrix in the chosen plaintext attack. In this paper, we propose a robust scheme adopting multiple measurement matrices to overcome this shortcoming. The matrices can be efficiently derived by applying random row exchanging to a basic one, which is also encoded into the fractional Fourier transform (FrFT) domain to improve the visual effect of wrongly decrypted images. Chaos-based pixel scrambling is added into our double FrFT cryptosystem to guarantee its nonlinearity. Simulation results have shown the security and effectiveness of our scheme.